

'''simple docstring'''





import argparse
import os
import re

import packaging.version


UpperCamelCase_    						=       """examples/"""
UpperCamelCase_    						=       {
    """examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
    """doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase_    						=       {
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
UpperCamelCase_    						=       """README.md"""






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Dict   , __magic_name__						:     Optional[Any]						)				->  Optional[Any]:
						with open(__magic_name__   , '''r'''   , encoding='''utf-8'''   , newline='''\n'''						) as f:
												lowercase :							List[Any]    		=f.read()
						lowercase		,	lowercase :							Union[str, Any]    		=REPLACE_PATTERNS[pattern]
						lowercase :							List[str]    		=replace.replace('''VERSION'''   , __magic_name__						)
						lowercase :							Any    		=re_pattern.sub(__magic_name__   , __magic_name__						)
						with open(__magic_name__   , '''w'''   , encoding='''utf-8'''   , newline='''\n'''						) as f:
												f.write(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]						)				->  Any:
						for folder, directories, fnames in os.walk(__magic_name__						):
												# Removing some of the folders with non-actively maintained examples from the walk
												if "research_projects" in directories:
																		directories.remove('''research_projects'''						)
												if "legacy" in directories:
																		directories.remove('''legacy'''						)
												for fname in fnames:
																		if fname.endswith('''.py'''						):
																								update_version_in_file(os.path.join(__magic_name__   , __magic_name__						)   , __magic_name__   , pattern='''examples'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     List[Any]=False						)				->  List[str]:
						for pattern, fname in REPLACE_FILES.items():
												update_version_in_file(__magic_name__   , __magic_name__   , __magic_name__						)
						if not patch:
												update_version_in_examples(__magic_name__						)






def 				_lowerCAmelCase			(   )				->  Optional[int]:
						lowercase :							str    		='''🤗 Transformers currently provides the following architectures'''
						lowercase :							List[str]    		='''1. Want to contribute a new model?'''
						with open(__magic_name__   , '''r'''   , encoding='''utf-8'''   , newline='''\n'''						) as f:
												lowercase :							Tuple    		=f.readlines()

						# Find the start of the list.
						lowercase :							Dict    		=0
						while not lines[start_index].startswith(_start_prompt						):
												start_index += 1
						start_index += 1

						lowercase :							Any    		=start_index
						# Update the lines in the model list.
						while not lines[index].startswith(_end_prompt						):
												if lines[index].startswith('''1.'''						):
																		lowercase :							List[str]    		=lines[index].replace(
																		    '''https://huggingface.co/docs/transformers/main/model_doc'''   , '''https://huggingface.co/docs/transformers/model_doc'''   , )
												index += 1

						with open(__magic_name__   , '''w'''   , encoding='''utf-8'''   , newline='''\n'''						) as f:
												f.writelines(__magic_name__						)






def 				_lowerCAmelCase			(   )				->  str:
						with open(REPLACE_FILES['''init''']   , '''r'''						) as f:
												lowercase :							Dict    		=f.read()
						lowercase :							Union[str, Any]    		=REPLACE_PATTERNS['''init'''][0].search(__magic_name__						).groups()[0]
						return packaging.version.parse(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple=False						)				->  List[Any]:
						lowercase :							Tuple    		=get_version()
						if patch and default_version.is_devrelease:
												raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''						)
						if default_version.is_devrelease:
												lowercase :							Optional[Any]    		=default_version.base_version
						elif patch:
												lowercase :							Any    		=f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
						else:
												lowercase :							Dict    		=f'''{default_version.major}.{default_version.minor + 1}.0'''

						# Now let's ask nicely if that's the right one.
						lowercase :							List[str]    		=input(f'''Which version are you releasing? [{default_version}]'''						)
						if len(__magic_name__						) == 0:
												lowercase :							List[str]    		=default_version

						print(f'''Updating version to {version}.'''						)
						global_version_update(__magic_name__   , patch=__magic_name__						)
						if not patch:
												print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''						)
												clean_main_ref_in_model_list()






def 				_lowerCAmelCase			(   )				->  int:
						lowercase :							str    		=get_version()
						lowercase :							int    		=f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
						lowercase :							int    		=current_version.base_version

						# Check with the user we got that right.
						lowercase :							Dict    		=input(f'''Which version are we developing now? [{dev_version}]'''						)
						if len(__magic_name__						) == 0:
												lowercase :							List[str]    		=dev_version

						print(f'''Updating version to {version}.'''						)
						global_version_update(__magic_name__						)
						print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''						)
						clean_main_ref_in_model_list()


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
							parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
							UpperCamelCase_    						=       parser.parse_args()
							if not args.post_release:
														pre_release_work(patch=args.patch)
							elif args.patch:
														print("""Nothing to do after a patch :-)""")
							else:
														post_release_work()













'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor


class  snake_case__      (					unittest.TestCase):




					def __init__(  self  :		Optional[Any]				, _A  :		int				, _A  :		List[Any]=7				, _A  :		Tuple=3				, _A  :		int=18				, _A  :		Union[str, Any]=30				, _A  :		Any=4_00				, _A  :		List[Any]=True				, _A  :		Optional[int]=None				, _A  :		Optional[Any]=True				, _A  :		Union[str, Any]=None				, )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[Any]          =       size if size is not None else {'''shortest_edge''': 20}
								UpperCAmelCase_      :       Dict          =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								UpperCAmelCase_      :       List[Any]          =       parent
								UpperCAmelCase_      :       List[Any]          =       batch_size
								UpperCAmelCase_      :       Optional[Any]          =       num_channels
								UpperCAmelCase_      :       Optional[Any]          =       image_size
								UpperCAmelCase_      :       Union[str, Any]          =       min_resolution
								UpperCAmelCase_      :       List[str]          =       max_resolution
								UpperCAmelCase_      :       Union[str, Any]          =       do_resize
								UpperCAmelCase_      :       Any          =       size
								UpperCAmelCase_      :       Union[str, Any]          =       do_center_crop
								UpperCAmelCase_      :       Any          =       crop_size





					def 		A      (  self  :		List[Any]      )    ->							List[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}






@require_torch
@require_vision
class  snake_case__      (					UpperCamelCase     ,				unittest.TestCase):
					a_        =					MobileNetVaImageProcessor if is_vision_available() else None




					def 		A      (  self  :		List[Any]      )    ->							List[str]:
								UpperCAmelCase_      :       Dict          =       MobileNetVaImageProcessingTester(self      )




					@property
					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()




					def 		A      (  self  :		List[str]      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(_A				, '''do_resize'''      )      )
								self.assertTrue(hasattr(_A				, '''size'''      )      )
								self.assertTrue(hasattr(_A				, '''do_center_crop'''      )      )
								self.assertTrue(hasattr(_A				, '''crop_size'''      )      )




					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								UpperCAmelCase_      :       Any          =       self.image_processing_class.from_dict(self.image_processor_dict      )
								self.assertEqual(image_processor.size				, {'''shortest_edge''': 20}      )
								self.assertEqual(image_processor.crop_size				, {'''height''': 18, '''width''': 18}      )

								UpperCAmelCase_      :       List[str]          =       self.image_processing_class.from_dict(self.image_processor_dict				, size=42				, crop_size=84      )
								self.assertEqual(image_processor.size				, {'''shortest_edge''': 42}      )
								self.assertEqual(image_processor.crop_size				, {'''height''': 84, '''width''': 84}      )




					def 		A      (  self  :		int      )    ->							List[str]:
								pass




					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								# Initialize image_processing
								UpperCAmelCase_      :       List[Any]          =       self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								UpperCAmelCase_      :       Optional[int]          =       prepare_image_inputs(self.image_processor_tester				, equal_resolution=_A      )
								for image in image_inputs:
											self.assertIsInstance(_A				, Image.Image      )

								# Test not batched input
								UpperCAmelCase_      :       Union[str, Any]          =       image_processing(image_inputs[0]				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )

								# Test batched
								UpperCAmelCase_      :       List[Any]          =       image_processing(_A				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )




					def 		A      (  self  :		str      )    ->							List[str]:
								# Initialize image_processing
								UpperCAmelCase_      :       Tuple          =       self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								UpperCAmelCase_      :       Any          =       prepare_image_inputs(self.image_processor_tester				, equal_resolution=_A				, numpify=_A      )
								for image in image_inputs:
											self.assertIsInstance(_A				, np.ndarray      )

								# Test not batched input
								UpperCAmelCase_      :       str          =       image_processing(image_inputs[0]				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )

								# Test batched
								UpperCAmelCase_      :       List[Any]          =       image_processing(_A				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )





					def 		A      (  self  :		Optional[int]      )    ->							Dict:
								# Initialize image_processing
								UpperCAmelCase_      :       Dict          =       self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								UpperCAmelCase_      :       Union[str, Any]          =       prepare_image_inputs(self.image_processor_tester				, equal_resolution=_A				, torchify=_A      )
								for image in image_inputs:
											self.assertIsInstance(_A				, torch.Tensor      )

								# Test not batched input
								UpperCAmelCase_      :       Any          =       image_processing(image_inputs[0]				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )

								# Test batched
								UpperCAmelCase_      :       List[str]          =       image_processing(_A				, return_tensors='''pt'''      ).pixel_values
								self.assertEqual(
								    encoded_images.shape				, (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )				, )


